The larger $R^2$ the better? [duplicate]

This question already has an answer here:

I want to show that the variable $X$ is significant.
In model 1, I use financial statements variables, other macroeconomic variables and $X$.
Here, $X$ is siginificant at a 10% level and $R^2$ is 0.731.
Since financial statements could have time lag, I have to check that effect so I made adjusted model 2.
In here $X$ is insignificant but $R^2$ is 0.721.
So, I can say that "since $R^2$ is decreased, model 1 is better and $X$ is significant variable"?

marked as duplicate by Momo, cardinalNov 27 '13 at 17:26

• It's difficult to know what you are seeking here. If $X$ is insignificant (at whatever level), it is insignificant. $R^2$ quantifies a different question. Change in $R^2$ has nothing to do with a significance calculation; the model doesn't remember what you did previously. – Nick Cox Nov 27 '13 at 13:12
• @NickCox If the models are nested, isn't looking at the change in $R^2$ equivalent to the partial F-test? – jsk May 15 '14 at 1:30
• @jsk Fair point, but only in the sense that looking at the difference between means is "equivalent to" a t-test. One is a descriptive calculation, the other a specific inference. – Nick Cox May 15 '14 at 18:20